Biomass crops as a soil amendment in cultivated histosols: Can we reach carbon equilibrium?
Bibliographic record
Abstract
Abstract Peatlands are known to perform essential economical, societal and regulating functions. Once they are drained to provide optimal crop growth conditions, however, a series of degradation processes is generated. Wind and water erosion, subsidence and soil organic matter oxidation are the main causes of degradation observed in cultivated histosols. This study evaluated the decomposition dynamics and chemical changes of three biomass crops during an in‐situ incubation in a cultivated histosol. The decomposition dynamics characterized in the field study were then used in a simulation to determine if sustainability could be reached by using biomass crops as a soil amendment. The results showed that an exponential decay fitting curve best represented the weight loss of sorghum [ Sorghum bicolor (L.) Moench] in the in‐situ bags over time, while a logistic fitting curve best represented that of miscanthus ( Miscanthus X giganteus ) and willow ( Salix miyabeana ). The quality of the crop determined the initial and overall decomposition dynamics observed. The loss of carbon from the crushed biomass crop was much more important in sorghum than in miscanthus and willow. The long‐term simulation of histosol amendment revealed that using miscanthus and willow at input rates of 7.5 and 10 T of carbon per year, respectively, would be sufficient to ensure sustainability. Improving knowledge on carbon loss in cultivated histosols as related to soil and crop management would help in developing a soil amendment program at the farm scale. In addition, more knowledge is needed to determine the impact of long‐term and successive amendment with biomass crops on the physical and biochemical properties of histosols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".